Micro-computed tomography of a 500-year-old tooth: technical note.
Bibliographic record
Abstract
OBJECTIVE: To determine whether micro-computed tomography (micro-CT) could be used to reconstruct ancient dental anatomy accurately and differentiate the enamel from the dentin, as well as to verify whether micro-CT could detect tooth disorders such as attrition or caries accurately. METHODS: Micro-CT imaging was performed, using a cone-beam micro-CT specimen scanner, on a 500-year-old human tooth found in a burial jar in the Cardomom Mountains in southwestern Cambodia. RESULTS: The occlusal surface of the tooth showed marked attrition, with the dentin extending close to the enamel layer on the crown. In addition to this, micro-CT images depicted calculus on the buccal surface and a cervical root caries lesion present on the distal surface. The sclerotic zone of the carious lesion (located deep in the destroyed dentin) and the dentin were effectively differentiated through excellent resolution and superior tissue contrast of the volume data set. Axial slices from apical to coronal show the carious lesion extending vertically along the dentin-enamel junction with an intact outer enamel surface. CONCLUSION: Micro-CT is a reproducible, nondestructive and highly accurate technique that can be successfully applied to the study of ancient teeth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".